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Anderson Accelerated Feasible Sequential Linear Programming

IFAC-PapersOnLine 2023年第2期56卷 7436-7441页

作者： David Kiessling Pieter Pas Alejandro Astudillo Panagiotis Patrinos Jan Swevers MECO Research Team Dept. of Mechanical Engineering KU Leuven. Flanders Make@KU Leuven 3001 Leuven Belgium Department of Electrical Engineering (ESAT) STADIUS Center for Dynamical Systems Signal Processing and Data Analytics KU Leuven

This paper proposes an accelerated version of Feasible Sequential Linear Programming (FSLP): the AA(d)-FSLP algorithm. FSLP preserves feasibility in all intermediate iterates by means of an iterative update strategy based on repeated evaluation of zero-order information. This technique was successfully applied to techniques such as model predictive control and Moving Horizon Estimation, but it can exhibit slow convergence. Moreover, keeping all iterates feasible in FSLP entails a large number of additional constraint evaluations. In this paper, Anderson Acceleration (AA(d)) is applied to the zero-order update strategy improving the convergence rate and therefore decreasing the number of constraint evaluations in the inner iterative procedure of the FSLP algorithm. AA(d) achieves an improved contraction rate in the inner iterations, with proven local linear convergence. In addition, it is observed that due to the improved zero-order update strategy, AA(d)-FSLP takes larger steps to find an optimal solution, yielding faster overall convergence. The performance of AA(d)-FSLP is examined for a time-optimal point-to-point motion problem of a parallel SCARA robot. The reduction of the number of constraint evaluations and overall iterations compared to FSLP is demonstrated.

关键词： Numerical methods for optimal control model predictive and optimization-based control predictive control

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MPC for Artificial Muscles Using FRIT based Optimized Pseudo Linearization model

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IFAC-PapersOnLine 2023年第2期56卷 7264-7269页

作者： Mikiya Sekine Satoshi Tsuruhara Kazuhisa Ito Graduate School of Engineering and Science Mechanical Engineering Shibaura Institute of Technology 307 Fukasaku Minuma Saitama 3378570 Japan Department of Machinery and Control Systems Shibaura Institute of Technology 307 Fukasaku Minuma Saitama 3378570 Japan

A McKibben-type artificial muscle exhibits strong asymmetric hysteresis characteristics. In a previous study, fictitious reference iterative tuning (FRIT) was applied to a 1-DOF rotational actuator applying these muscles because it was able to determine the controller parameters using only a pair of single closed-loop data. However, it is generally difficult to design a reference model properly without information on the plant, which is strongly related to the control performance, and sometimes this leads to performance degradation. To solve this problem, in this study, we propose a method combining FRIT and model predictive control (MPC). First, we define the pseudo linearization (PL) model, which is adopted as a predictor in MPC, and is used to optimization between the model and design a control system by matching the closed-loop system to the PL model using FRIT. Additionally, it can consider input constraints by combined MPC.

关键词： Data-driven control model predictive and optimization-based control Motion control systems Artificial muscle controller parameter tuning Pseudo Linearization

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Q-MPC: stable and efficient reinforcement learning using model predictive control

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IFAC-PapersOnLine 2023年第2期56卷 2727-2732页

作者： Tae Hoon Oh Department of Chemical Engineering Kyoto University Kyoto Japan

There is a growing interest in developing an efficient data-driven control method that can be implemented into digitized manufacturing processes. model-free reinforcement learning (RL) is a machine learning method that can directly learn the optimal control policy from the process data. However, the model-free RL shows higher cost variance than the model-based method and may require an infeasible amount of data to learn the optimal control policy. Motivated by the fact that the system identification to linear model shows high data efficiency and stable performance, this paper proposes combining the linear model predictive control (MPC) with Q-learning. This combined scheme, Q-MPC, can improve the control performance more stably and safely. For the case study, linear MPC, Q-MPC, DDPG, TD3, and SAC methods are applied to the nonlinear benchmark system, mainly focusing on the learning speed and cost variance.

关键词： model predictive and optimization-based control Reinforcement learning control Process modeling and identification Data-based control Learning for control predictive control

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Optimal privacy protection of mobility data: a predictive approach

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IFAC-PapersOnLine 2023年第2期56卷 11015-11020页

作者： Emilio Molina Mirko Fiacchini Sophie Cerf Bogdan Robu Univ. Grenoble Alpes CNRS Grenoble INP GIPSA-lab Grenoble 38000 France Univ. Lille Inria CNRS Centrale Lille UMR 9189 CRIStAL Lille F-59000 France

Location data are extensively used to provide geo-personalized contents to mobile devices users. Sharing such personal data is a major threat to privacy, with risks of re-identification or inference of sensitive information. Location data broadcasted to services can be sanitized, i.e., by adding noise to spatial coordinates. Such protection mechanisms from the literature are widely generic, e.g., not specific to a user and mobility properties. In this work, we advocate that taking into account the specificities of location data (temporal correlation, human mobility patterns, etc.) enables to gain in the privacy-utility trade-off. specifically, using future mobility prediction greatly improves privacy. We present a novel protection mechanism, based on model predictive control (MPC). The sanitized location is optimally computed so that it maximizes privacy while guaranteeing a utility loss constraint, for present and future locations. Our formulation explicitly takes into account non-constant sampling time, due to moments when no location data is broadcasted. We evaluate experimentally our control on real mobility dataset and compare to the state of the art. Results show that with knowledge of user's future mobility over a few of minutes, we can gain up to 10% of privacy compared to state of the art, while preserving data utility.

关键词： Security and privacy model predictive and optimization-based control predictive control

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Data-driven adaptive two-degree of freedom control of interconnected systems for reference tracking

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IFAC-PapersOnLine 2023年第2期56卷 2713-2718页

作者： Felix Häusser Andreas Himmel Rolf Findeisen

Many industrial processes consist of a series of interconnected components. While the components are often individually controllable, the potentially complex interaction mechanisms limit the design options for a process-wide controller. Typical reasons are that parts of the component interactions are difficult to model or that the overall, detailed physical model is very complex. Due to these challenges, using model-based control approaches, such as model predictive control, often becomes infeasible. To this end, an adaptive two-degree of freedom control concept for reference tracking tasks is proposed. A data-driven surrogate model of the coupled system is trained, focusing on the reference tracking objective, and exploiting the model structure. This surrogate model is used in a two-degree of freedom control concept, splitting the tasks into reference tracking and disturbance rejection. The proposed control concept applies to a broad class of reference tracking processes subject to partially unknown system dynamics. It is tested exemplarily to control an interconnected three-tank system.

关键词： control of distributed systems model predictive and optimization-based control Machine learning and data analytics in process control Nonlinear process control

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A Review of Parameterised MPC Algorithms

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IFAC-PapersOnLine 2023年第2期56卷 7692-7697页

作者： John A. Rossiter Oscar J. Gonzalez-Villareal Mohammad A.M. Sarbini Department of Automatic Control and Systems Engineering University of Sheffield Sheffield S1 3JD UK Electrical and Electronic Engineering Programme Area Universiti Teknologi Brunei

This paper provides an evaluation and comparison of popular parameterised model predictive control approaches that have been proposed in the literature in recent years. Using the Generalised predictive control (GPC) algorithm as the baseline algorithm, the paper sets out a number of performance criteria to compare and contrast with several other MPC approaches. Numerical examples use 100 random samples of 2, 3, and 4-state models and the approaches are compared using the selected performance criteria.

关键词： predictive control model predictive and optimization-based control Monte Carlo methods

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Safety-Critical control for Ensemble Systems

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IFAC-PapersOnLine 2023年第2期56卷 3152-3157页

作者： Yang Guo Felix Petzke Philipp Rumschinski Stefan Streif Technische Universität Chemnitz Automatic Control and System Dynamics Lab Hochschule Furtwangen Faculty of Mechanical and Medical Engineering

In this paper, we derive set constraints for a reduced order model and augment them into a model predictive control (MPC) scheme to ensure safe operation of the large-scale ensemble system. For the control feedback, only the aggregated information of the whole system is required. For the constraint satisfaction, we consider an adaptive tube formulation to characterize the deviation between the reduced order model and the ensemble system. Employing the robust control invariant set, we ensure recursive feasibility and initial feasibility under an easily verifiable condition.

关键词： robust control model predictive and optimization-based control constrained control linear systems feasibility ensemble systems

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Trajectory-dependent control Lyapunov Functions for Discrete-time Switched Systems ⋆

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IFAC-PapersOnLine 2023年第2期56卷 4150-4155页

作者： Gerardo Becerra Minh Tu Pham Diego Patino Xuefang Lin-Shi Universidad Nacional Abierta y a Distancia UNAD Bogotá D.C. Colombia Univ Lyon INSA Lyon Université Claude Bernard Lyon 1 Ecole Centrale de Lyon CNRS Ampère UMR5005 69621 Villeurbanne France Department of Electronics Engineering Pontificia Universidad Javeriana Bogotá D.C. Colombia

A method for control of discrete-time switched systems based on parameterized Lyapunov functions is presented. A polynomial representation of the switched system is formulated, and the method of moments is applied to obtain a relaxed model. The control problem is recast as an online receding horizon optimization problem where the Lyapunov function parameters and moment sequences are the decision variables. A novel procedure to synthesize the switching control signal from the recovered measure is described. The method is illustrated in simulation for the case of a pulse-width modulated multicellular converter, showing good reference tracking results.

关键词： control of switched systems Lyapunov methods model predictive and optimization-based control polynomial methods modeling and simulation of power systems

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Minimum-Variance model predictive control for Dual Fluidized Bed Circulation control

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IFAC-PapersOnLine 2023年第2期56卷 2701-2706页

作者： Lukas Stanger Alexander Schirrer Alexander Bartik Martin Kozek TU Wien Institute of Mechanics and Mechatronics Getreidemarkt 9 1060 Vienna Austria TU Wien Institute of Chemical Environmental and Bioscience Engineering Getreidemarkt 9 1060 Vienna Austria

Dual fluidized bed steam gasification enables the production of gaseous energy carriers from woody biomass or biogenic residues. The circulation of bed material in dual fluidized bed gasifiers strongly affects the process behavior. Therefore, precise control of the bed material circulation is desired. This paper presents a control algorithm addressing two aspects of the given problem setting: On the one hand, redundant control actuators are available. Typically, there are several air streams to the reactors influencing the bed material circulation. On the other hand, only black box models with uncertainties in their model parameters are available for model-based control design. The presented control algorithm uses a model predictive controller considering known uncertainties in the model parameters and drives the process in a region with the lowest model uncertainties. This results in an improvement of the closed-loop performance when the actual plant deviates from the internal model used for the model predictive control predictions. Simulations show 66 % less offset from the design trajectory with the presented algorithm when compared to a standard model predictive controller.

关键词： model predictive and optimization-based control Process control applications model uncertainties control allocation Dual fluidized bed gasification

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Application of Data-Driven Economic NMPC on a Gas Lifted Well Network 16

Application of Data-Driven Economic NMPC on a Gas Lifted Wel...

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16th IFAC Symposium on Advanced control of Chemical Processes (ADCHEM)

作者： Andersen, Joakim Rostrup Imsland, Lars Norwegian Univ Sci & Technol Dept Engn Cybernet Trondheim Norway

The Daily Production optimization (DPO) problem is the task of maximizing production of hydrocarbons subject to operational constraints. Handling of uncertainty in model structure and parameters is of high importance to the usefulness of the solution. Ignoring these challenges will, most likely, render the solution either infeasible or the solution will not be an optimum of the plant. We suggest to apply a data-driven methodology to use state- and output-measurements from the plant to iteratively update the Optimal control Problem (OCP) which are used to control the plant. The goal of the method is to tune the OCP such that the solution will go towards an optimum of the plant as the parameters are being updated. A Reinforcement Learning updating technique is used to update the optimization formulation. Copyright (C) 2021 The Authors.

关键词： nonlinear process control reinforcement learning control model predictive and optimization-based control

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